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Record W2803040682 · doi:10.5539/jas.v10n6p86

Nutritional Status and Productive Components of Maize Fertilized With Sewage Sludge

2018· article· en· W2803040682 on OpenAlexvenueno aff
Carlos César Breda, Renan Francisco Rimoldi Tavanti, Onã da Silva Freddi, Tauan Rimoldi Tavanti, Aline Regina Piedade, Denise Mahl, Iraê Amaral Guerrini, Rogério Carlos Traballi

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSewage sludgeAgronomySowingUltisolRandomized block designCropPhosphorusSewageFertilizerEnvironmental scienceSoil waterBiologyChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

The response of crops to the application of biofertilizers in the soil is not yet well established. Therefore, we carried out this study with the objective of evaluating the productive potential of maize fertilized with doses of sewage sludge, comparing with the use of mineral fertilizer over four agricultural seasons. The experiment was conducted in a Red Dystroferric Nitosol (Ultisol) in a randomized block design and repeated measures in time. The treatments consisted of five doses of sewage sludge and one treatment using nitrogen, phosphorus and potassium in formulation. All fertilizations were carried out at maize sowing at four planting times (summer and 2nd crop of 1st agricultural year and, summer and 2nd crop of 2nd agricultural year) and replicates. The results showed a cumulative increase in the P, Ca, Mg and Zn leaf concentrations over the four seasons crops, when the sewage sludge was used in the 30 and 40 Mg ha-1 doses. However, reductions in plant height, weight and maize cob diameter, mass and grain yield were observed at the end of the last harvest. The treated sewage sludge is a viable alternative for maize fertilization in the first harvest, provides increases in grain yield in the order of 1.35 Mg ha-1 and can be recommended instead of NPK mineral fertilizers in built-up fertility soils.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.230
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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